What is a confusion matrix, and how is it used in classification problems?

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Supervised and Unsupervised Learning are two primary types of machine learning, differing mainly in how they process and learn from data.

A confusion matrix is a performance measurement tool used to evaluate the results of a classification algorithm. It summarizes the performance of a classification model by comparing the predicted labels with the true labels. The matrix allows you to see not only the errors made by the model but also the types of errors.

Structure of a Confusion Matrix:

A confusion matrix for a binary classification problem (two classes: positive and negative) looks like this:

Predicted Positive Predicted Negative

Actual Positive True Positive (TP) False Negative (FN)

Actual Negative False Positive (FP) True Negative (TN)

Here’s what each term means:

True Positive (TP): The number of instances where the model correctly predicted the positive class.

True Negative (TN): The number of instances where the model correctly predicted the negative class.

False Positive (FP): The number of instances where the model incorrectly predicted the positive class (Type I error).

False Negative (FN): The number of instances where the model incorrectly predicted the negative class (Type II error).

Usage in Classification Problems:

The confusion matrix is primarily used in classification problems to evaluate the effectiveness of a model. It provides several important metrics:

Accuracy: The proportion of correct predictions (TP + TN) out of all predictions.

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